Many process facilities (e.g., a manufacturing plant, a mineral or crude oil refinery, etc.) are managed using distributed control systems. Typical contemporary control systems include numerous modules tailored to monitor and/or control various processes of the facility. Conventional means link these modules together to produce the distributed nature of the control system. This affords increased performance and a capability to expand or reduce the control system to satisfy changing facility needs.
Process facility management providers, such as Honeywell, Inc., develop control systems that can be tailored to satisfy wide ranges of process requirements (e.g., global, local or otherwise) and facility types (e.g., manufacturing, warehousing, refining, etc.). Such providers have two principle objectives. The first objective is to centralize control of as many processes as possible to improve an overall efficiency of the facility. The second objective is to support a common interface that communicates data among various modules controlling or monitoring the processes, and also with any such centralized controller or operator center.
Each process, or group of associated processes, has one or more input characteristics (e.g., flow, feed, power, etc.) and one or more output characteristics (e.g., temperature, pressure, etc.) associated with it. Model predictive control ("MPC") techniques have been used to optimize certain processes as a function of such characteristics. One MPC technique uses algorithmic representations of certain processes to estimate characteristic values (represented as parameters, variables, etc.) associated with them that can be used to better control such processes. In recent years, physical, economic and other factors have been incorporated into control systems for these associated processes.
Examples of such techniques are described in U.S. Pat. No. 5,351,184, entitled "Method of Multivariable Predictive Control Utilizing Range Control;" U.S. Pat. No. 5,561,599, entitled "Method of Incorporating Independent Feedforward Control in a Multivariable Predictive Controller;" U.S. Pat. No. 5,572,420, entitled "Method of Optimal Controller Design of Multivariable Predictive Control Utilizing Range Control;" and U.S. Pat. No. 5,574,638, entitled "Method of Optimal Scaling of Variables in a Multivariable Predictive Controller Utilizing Range Control," all of which are commonly owned along by the assignee of the present invention and incorporated herein by reference for all purposes (the foregoing issued patents and U.S. patent application Ser. No. 08/490,499, previously incorporated herein by reference, are collectively referred to hereafter as the "Honeywell Patents and Application").
The distributed control systems used to monitor and control a process are frequently linked by common communication pathways, such as by a local area network (LAN) architecture or by a wide area network (WAN) architecture. When a requesting node needs a datum from a responding node, it issues a request for the datum across the network and the responding node then returns the datum back across the network. Many process control systems use a supervisory control LAN or WAN integrated with one or more process control networks. The process control networks contain the basic raw data required by the supervisory control network and other process control networks.
Typically, a supervisory controller is linked to a flexible array of processor controllers using communication drivers matched to the specific processor controller being interfaced. The supervisory controller maps the essential data of these process controllers into a homogeneous database controlled by the supervisory controller for consistent storage and access by individual process controller or by any client application being executed by the supervisory controller.
Generally, a supervisory control network joins process control network(s) by polling (scanning) at a fixed or flexible interval for all data that is mapped by user configuration to the supervisory control systems database. One or more server nodes physically join the supervisory control local and wide area networks to the process control network(s). These server nodes are the data repositories for all client access among the supervisory client nodes. A problem is encountered, however, when the quantity of information in the process control network(s) is greater than that which can be polled all together at an acceptable update rate. In such a case it may take two or more pollings of a process control network to retrieve all of the data required by a client application. This is particularly true for those client applications where users may switch back and forth between screens (views) displaying data from a process control network.
There is therefore a need in the art for improved supervisory control systems that provide one or more client applications with faster access to information in a one or more process control systems. In particular, there is a need in the art for improved supervisory control systems that provide one or more client applications with faster data access than can be achieved by the periodic polling of the data.